Thermoacoustic imaging is a fast evolving non-invasive modality for high-resolution mapping of local energy deposition in tissue. In particular, optoacoustic (or photoacoustic) tomography aims at reconstructing maps of local optical absorption coefficient of tissue, which can subsequently be related to the concentration of certain intrinsic and exogenously-administered biomarkers and probes. The imaging is performed by illuminating the object or region of interest with short high-power electromagnetic pulses, in particular laser pulses, thus creating an instantaneous temperature elevation and thermal expansion within it. As a result, high-frequency acoustic fields are formed that propagate towards the object's boundary where they can be subsequently recorded. In this way, an image representing local energy deposition within the object can be reconstructed by collecting tomographic information around the object and using optoacoustic inversion algorithms.
While image formation in optoacoustics can be performed in several ways, they require some mathematical inversion for image formation from raw measurements of the optoacoustic response. Ideally, the reconstructed image should represent a quantitative map of the underlying absorption properties of the imaged tissue, which can be subsequently related to distribution of various tissue chromophores and biomarkers. However, in most cases, the detected acoustic signals represent the overall local energy deposition in the imaged object. Thus, the initial optoacoustic image requires further analysis to extract the relevant information. One apparent difficulty is the highly heterogeneous nature of common biological tissues in the visible and near-infrared spectra. This in turn causes highly non-uniform distribution of excitation light within the imaged object owing to scattering and attenuation. In addition, inaccuracies in the currently used optoacoustic inversion models further hinder image quantification.
With further detail, current optoacoustic inversion techniques, which produce the deposited energy image from the acoustic fields, suffer from quantification inaccuracy, low spatial resolution, or inflexibility owing to the absence of appropriate methods to accurately account for experimental and physical propagation factors. Algorithms that currently exist for inversion and image formation can be divided into two groups: those based on a closed form analytical solution of the optoacoustic wave equation and those based on numerical calculations.
The first kind of algorithms refers to back-projection algorithms in which the reconstruction of the optoacoustic image is given as an integral over the measured signals similar to linear Radon-based transformations. These reconstruction methods are usually based on several approximations to the exact optoacoustic equation, thus creating substantial artifacts in the reconstruction. Moreover, the back-projections algorithms are incapable of incorporating multiple instrumentation-based factors into the inversion. For example, it may not be possible to directly take into account the frequency response of the ultrasonic detector or its finite size in the back-projection algorithms.
The second kind of algorithms is model-based inversion in which the acoustic propagation problem is solved numerically and iteratively in either the frequency or time domains by using, e.g., finite-elements method. Model-based thermoacoustic inversion schemes were previously attempted by H. Jiang et al. (“J. Opt. Soc. Am.,” vol. A 23, 2006, p. 878-888) and by G. Paltauf et al. (“The Journal of the Acoustical Society of America,” vol. 112(4), 2002, p. 1536-44). However, the computational complexity involved with these particular methods has limited their achievable resolution and hindered practical implementations.
Since in most realistic imaging scenarios it is practically impossible to uniformly illuminate the entire region of interest, the initially formed image will represent a coupled map of energy deposition in tissue and absorption, rather than the required quantified absorption coefficient values. In other words, the thermoacoustic image will comprise a product between the optical absorption and the light fluence within the object. Thus, targets deep in the object may appear weaker than targets having similar optical absorption but located close to the illuminated surface. Quantitative reconstructions, especially of volumetric phenomena, were not previously possible owing to limitations in both optoacoustic inversion algorithms, image normalization methods and corresponding system implementations. These inaccuracies limit applicable areas of conventional (qualitative) optoacoustic imaging. Consequently, systems reported so far for opto-acoustic imaging compromise the image quality and quantification, a performance that worsens with depth or volumetric imaging.
Moreover, when analyzing multi-spectral optoacoustic images, i.e., images obtained for several excitation wavelengths, spectral changes in the light fluence may dominate the optoacoustic images and obfuscate the absorption spectrum of targets of interests. This limits the use of Multi-Spectral Optoacoustic Tomography (MSOT), which is described, e.g., in PCT/EP2008/006142 (unpublished on the priority date of the present specification), for mapping the concentration of various targets with spectrally dependant absorptions.
Methods have been proposed for the extraction of the absorption coefficient and quantification improvement. Some approaches are based on solving the diffusion equation that governs light propagation to find and correct for light distribution within the object (B. T. Cox et al. in “Applied Optics,” vol. 45, p. 1866-1875, 2006; B. T. Cox B T et al in “Proc SPIE,” 6437, 64371T-1-10, 2007). Once a hypothesized light distribution solution is found, it is used to normalize the optoacoustic image and to extract the absorption coefficient. As a further step, the extracted absorption can be used to recalculate the light distribution within the object, which in turn is used to re-normalize the optoacoustic image. This process can be repeated in an iterative manner until convergence is achieved.
However, the above methods rely on empirical assumptions regarding optical properties of the tissue and other experimental parameters. Therefore they suffer from convergence instability that limits the ability for robust quantification accuracy (Jetzfellner et al. in “Appl. Phys. Lett.,” 95(1), 2009).
The main deficiency of optoacoustic image normalization methods is that they rely on a light fluence distribution that is modeled based on hypothesized light propagation equations, such as the light diffusion equation. However, these equations require accurate prior knowledge of the optical properties of the medium, which are usually largely unknown, in particular scattering but also absorption. In most cases, an estimated value or structure is prescribed to the optical parameters of the medium. In practice, it has been found however that even small errors in the assigned optical properties can lead to large errors in the reconstruction. Moreover, if an iterative self-correcting approach is applied, convergence to accurate values is rarely possible owing to modeling inaccuracies. Thus, as to date, reliable performance has not yet been demonstrated using these techniques for in-vivo data, i.e., for media where light distribution is spatially heterogeneous.
The above limitations are not restricted to optoacoustics. The corresponding disadvantages generally occur in other thermoacoustic imaging methods wherein, e.g., radiofrequency pulses are used for delivering energy to the object instead of the object illumination with laser pulses.
It could therefore be helpful to provide improved methods and preferred geometry for thermoacoustic imaging capable of avoiding disadvantages and restrictions of conventional qualitative reconstruction techniques. In particular, it could be helpful to provide accurate thermoacoustic imaging methods capable of quantitative imaging with increased precision and reproducibility, not only in surface and subsurface regions, but also volumetrically in entire objects. Furthermore, it could be helpful to provide thermoacoustic imaging device implementations capable of avoiding disadvantages and restrictions of conventional techniques.